This specification relates to traffic prediction for road networks.
The two most important commodities of the 21st century are time and energy; traffic congestion wastes both. Several disciplines, such as in transportation science, civil engineering, policy planning, and operations research have studied the traffic congestion problem through mathematical models, simulation studies and field surveys. However, due to the recent sensor instrumentations of road networks in major cities as well as the vast availability of auxiliary commodity sensors from which traffic information can be derived, e.g., CCTV (closed-circuit television) cameras, GPS (global positioning system) devices, for the first time a large volume of real-time traffic data at very high spatial and temporal resolutions has become available. While this is a gold mine of data, the most popular utilization of this data is to simply visualize and utilize the current real-time traffic congestion on online maps, car navigation systems, sig-alerts, or mobile applications. However, the most useful application of this data is to predict the traffic ahead of you during the course of a commute. This predictive information can be either used by a driver directly to avoid potential gridlocks or consumed by a smart route-planning algorithm to ensure a driver picks the best route from the start. Using traffic information that avoids congestion can potentially save consumers substantial amounts of time and money.